Get CRO wins when you can't reach A/B significance
When traffic is low, classic A/B testing often can't reach statistical significance in reasonable time. This recipe builds an experimentation plan using methods better suited to low volume (qualitative testing, higher-impact changes, sequential tests, micro-conversions, and clearer hypotheses).
Build a CRO experimentation plan for a low-traffic site. Output: - Feasibility assessment (what effect sizes are detectable with this volume) - Recommended testing methods (not just A/B—include qualitative, sequential, micro-conversions) - Backlog of 10 experiments (hypothesis + KPI + effort + expected learning) - Measurement plan using micro-conversions and qualitative signals Inputs: - Monthly visitors: - Monthly conversions: - Primary conversion: - Top pages: - Constraints (engineering/design capacity):
This recipe prevents teams from wasting months on underpowered tests.
Turn metrics into decisions (not anxiety)
Interprets analytics across platforms and converts them into prioritized actions. Built for creators who feel overwhelmed or confused by metrics like CTR, impressions, watch time, retention, saves, shares, and reach.
Diagnose missing conversions and "flying blind" measurement fast
Use this when the numbers don't match: ad platforms over/under-report, GA4 looks off, CRM revenue doesn't reconcile, or privacy changes (ATT/cookie loss/consent) have degraded tracking. It produces a root-cause shortlist, a "what to trust" guidance note, and a prioritized fix plan.
Label mistakes so patterns become obvious
Traders often report that profitability improved only after tracking mistakes (not just P&L). This recipe forces a mistake tag on every trade and compiles a mistake leaderboard.
Converts tags + stats into one concrete rule change
Traders often recommend a weekly review to spot repetitive patterns (revenge trades after first loss, overtrading during lunch, etc.). This recipe compiles the week into a short brief and proposes one fix.